Book Image

Mastering Reinforcement Learning with Python

By : Enes Bilgin
Book Image

Mastering Reinforcement Learning with Python

By: Enes Bilgin

Overview of this book

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.
Table of Contents (24 chapters)
Section 1: Reinforcement Learning Foundations
Section 2: Deep Reinforcement Learning
Section 3: Advanced Topics in RL
Section 4: Applications of RL

Focusing on generalization in reinforcement learning

The core goal in most machine learning projects is to obtain models that will work beyond training, and under a broad set of conditions during test time. Yet, when you start learning about RL, efforts to prevent overfitting and achieve generalization are not always at the forefront of the discussion, as opposed to how it is with supervised learning. In this section, we discuss what leads to this discrepancy, describe how generalization is closely related to partial observability in RL, and present a general recipe to handle these challenges.

Generalization and overfitting in supervised learning

When we train an image recognition or forecasting model, what we really want to achieve is high accuracy on unseen data. After all, we already know the labels for the data at hand. We use various methods to this end:

  • We use separate training, dev, and test sets, for model training, hyperparameter selection, and model performance...